Grigor Nalbandyan
Verified Expert in Engineering
Data Scientist and AI Developer
Munich, Germany
Toptal member since July 22, 2022
Grigor is a data scientist with over three years of industry experience in applied machine learning. He focuses on deep learning, computer vision, and natural language processing and has co-authored a paper that was accepted at the Institute of Electrical and Electronics Engineers (IEEE). Grigor is also completing his master's in data engineering and analytics at the Technical University of Munich in Germany.
Portfolio
Experience
- PyTorch - 3 years
- Python 3 - 3 years
- Transformers - 3 years
- Scikit-learn - 3 years
- Computer Vision - 3 years
- Natural Language Processing (NLP) - 3 years
- Deep Learning - 3 years
- Artificial Intelligence (AI) - 3 years
Preferred Environment
PyTorch, NumPy, Pandas, Scikit-learn, Transformers, Python 3, Git, Machine Learning, Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), Data Science, Artificial Intelligence (AI), Computer Vision, Deep Learning, Object Detection, Computer Vision Algorithms, JSON, CSV, BERT, Word2Vec, JSTransformers
The most amazing...
...project I've worked on is an unstructured document information extraction based on computer vision.
Work Experience
Machine Learning Specialist
WebbFontaine
- Developed a tool for the extraction of key information from images.
- Co-authored the paper: "Tokengrid: Towards More Efficient Data Extraction from Unstructured Documents," which was accepted at the IEEE.
- Built an optical character recognition (OCR) engine for text detection and recognition from images.
- Classified product descriptions into greater than 3,000 classes of harmonized system codes.
Experience
Key Information Extraction from Scanned Images
https://ieeexplore.ieee.org/document/9749071As a result, we wrote the paper: "Tokengrid: Towards More Efficient Data Extraction from Unstructured Documents," which was accepted at the IEEE.
Research of Pruning and Quantization Effect on CNN-Based Models
This project focuses on the following:
• The latency vs. accuracy tradeoff when pruning a CNN model.
• The latency vs. accuracy tradeoff when quantizing a CNN model.
• The effect of hardware potential speed-up.
• Comparison of various frameworks: PyTorch, ONNX, OpenVino, NeuralMagic
Search Engine for Classification of Product Descriptions Into Harmonized System
Research of Neural Architecture Search (NAS) Algorithms
Skills
Libraries/APIs
PyTorch, NumPy, Pandas, Scikit-learn, Matplotlib
Tools
Jupyter, Plotly, Open Neural Network Exchange (ONNX), Git, OpenVINO
Languages
Python 3, Python
Storage
JSON
Platforms
Docker
Other
CSV, Transformers, Computer Vision, Machine Learning, Natural Language Processing (NLP), Data Science, Artificial Intelligence (AI), Deep Learning, Convolutional Neural Networks (CNNs), Computer Vision Algorithms, Text Classification, Linear Regression, Logistic Regression, Decision Trees, Gradient Boosting, Data Visualization, Neural Networks, Recurrent Neural Networks (RNNs), Long Short-term Memory (LSTM), fastText, Quantization, Neural Network Pruning, BERT, Word2Vec, JSTransformers, Generative Pre-trained Transformers (GPT), Object Detection, Artificial Neural Networks (ANN)
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